Bloom filters and other probabilistic data structures are often utilized to quickly and compactly determine whether an element is part of a set of elements or not. For instance, an administrator of a database can add database entries into a Bloom filter, which may then be used to support database queries by identifying whether a database entry is possibly within the database or is definitely not within the database. Bloom filters have the inherent advantage of being memory efficient, as entries added to Bloom filters are hashed in a manner that the resulting value merely triggers bits within the Bloom filter from zero to one. However, once an entry has been added to a Bloom filter, the entry cannot be removed, as changing Bloom filter bits from one to zero may impact other entries within the Bloom filter, sacrificing the integrity of the Bloom filter. Using counting filters with n-bit counters for each Bloom filter segment may be utilized to remove entries from the Bloom filter but also sacrifice memory efficiency, as each segment of the Bloom filter may now comprise many more bit values, increasing the size of the Bloom filter.